Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations6955
Missing cells145049
Missing cells (%)47.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory352.0 B

Variable types

Numeric10
Categorical14
Unsupported15
Text4
Boolean1

Alerts

TOW_AWAY has constant value "04:00PM-06:00PM MON-FRI"Constant
TRAVEL_DIRECTION has constant value "S"Constant
NUMBEROFSPACES has constant value "1"Constant
HAS_SENSOR has constant value "False"Constant
METER_STATE has constant value "ACTIVE"Constant
SPACE_STATE has constant value "ACTIVE"Constant
BASE_RATE is highly overall correlated with G_PASSPORT_ZONES and 4 other fieldsHigh correlation
DIR is highly overall correlated with G_PASSPORT_ZONES and 4 other fieldsHigh correlation
G_DISTRICT is highly overall correlated with G_PASSPORT_ZONES and 5 other fieldsHigh correlation
G_PASSPORT_ZONES is highly overall correlated with BASE_RATE and 14 other fieldsHigh correlation
G_PM_ZONE is highly overall correlated with BASE_RATE and 12 other fieldsHigh correlation
G_SUBZONE is highly overall correlated with G_PASSPORT_ZONES and 9 other fieldsHigh correlation
INSTALLED_ON is highly overall correlated with G_PASSPORT_ZONES and 7 other fieldsHigh correlation
LATITUDE is highly overall correlated with METER_ID and 2 other fieldsHigh correlation
LOCK_ is highly overall correlated with G_DISTRICT and 6 other fieldsHigh correlation
LONGITUDE is highly overall correlated with G_PASSPORT_ZONES and 5 other fieldsHigh correlation
METER_ID is highly overall correlated with BASE_RATE and 17 other fieldsHigh correlation
METER_TYPE is highly overall correlated with G_PASSPORT_ZONES and 7 other fieldsHigh correlation
OBJECTID is highly overall correlated with BASE_RATE and 15 other fieldsHigh correlation
PARK_NO_PAY is highly overall correlated with G_PASSPORT_ZONES and 5 other fieldsHigh correlation
PAY_POLICY is highly overall correlated with G_PASSPORT_ZONES and 5 other fieldsHigh correlation
SPACE_NUMBER is highly overall correlated with BASE_RATE and 17 other fieldsHigh correlation
VENDOR is highly overall correlated with G_PASSPORT_ZONES and 7 other fieldsHigh correlation
X is highly overall correlated with G_PASSPORT_ZONES and 5 other fieldsHigh correlation
Y is highly overall correlated with LATITUDE and 2 other fieldsHigh correlation
_id is highly overall correlated with G_PASSPORT_ZONES and 6 other fieldsHigh correlation
VENDOR is highly imbalanced (87.1%)Imbalance
PAY_POLICY is highly imbalanced (56.1%)Imbalance
PARK_NO_PAY is highly imbalanced (64.4%)Imbalance
LOCK_ is highly imbalanced (97.9%)Imbalance
METER_TYPE is highly imbalanced (87.1%)Imbalance
G_DISTRICT is highly imbalanced (98.2%)Imbalance
BASE_RATE is highly imbalanced (99.6%)Imbalance
INSTALLED_ON is highly imbalanced (76.7%)Imbalance
METER_ID has 6831 (98.2%) missing valuesMissing
PRE_PAY has 6955 (100.0%) missing valuesMissing
GREEN_DOME has 6955 (100.0%) missing valuesMissing
TOW_AWAY has 6901 (99.2%) missing valuesMissing
STREET_CLEANING has 6955 (100.0%) missing valuesMissing
LOCK__ has 6955 (100.0%) missing valuesMissing
TRAVEL_DIRECTION has 6953 (> 99.9%) missing valuesMissing
FROM_INTERSECTION has 6955 (100.0%) missing valuesMissing
TO_INTERSECTION has 6955 (100.0%) missing valuesMissing
SPACE_NUMBER has 6831 (98.2%) missing valuesMissing
G_PASSPORT_ZONES has 6368 (91.6%) missing valuesMissing
G_PM_ZONE has 6771 (97.4%) missing valuesMissing
POLE_MOUNT has 6955 (100.0%) missing valuesMissing
YOKE has 6955 (100.0%) missing valuesMissing
HOUSING_TYPE has 6955 (100.0%) missing valuesMissing
HOUSING_MANUFACTURER has 6955 (100.0%) missing valuesMissing
SIDEWALKGE has 6955 (100.0%) missing valuesMissing
COIN_SLOTLE has 6955 (100.0%) missing valuesMissing
METER_CONDITION has 6955 (100.0%) missing valuesMissing
PERMIT_RATE has 6955 (100.0%) missing valuesMissing
PURCHASED_DATE has 6955 (100.0%) missing valuesMissing
_id is uniformly distributedUniform
OBJECTID is uniformly distributedUniform
_id has unique valuesUnique
OBJECTID has unique valuesUnique
PRE_PAY is an unsupported type, check if it needs cleaning or further analysisUnsupported
GREEN_DOME is an unsupported type, check if it needs cleaning or further analysisUnsupported
STREET_CLEANING is an unsupported type, check if it needs cleaning or further analysisUnsupported
LOCK__ is an unsupported type, check if it needs cleaning or further analysisUnsupported
FROM_INTERSECTION is an unsupported type, check if it needs cleaning or further analysisUnsupported
TO_INTERSECTION is an unsupported type, check if it needs cleaning or further analysisUnsupported
POLE_MOUNT is an unsupported type, check if it needs cleaning or further analysisUnsupported
YOKE is an unsupported type, check if it needs cleaning or further analysisUnsupported
HOUSING_TYPE is an unsupported type, check if it needs cleaning or further analysisUnsupported
HOUSING_MANUFACTURER is an unsupported type, check if it needs cleaning or further analysisUnsupported
SIDEWALKGE is an unsupported type, check if it needs cleaning or further analysisUnsupported
COIN_SLOTLE is an unsupported type, check if it needs cleaning or further analysisUnsupported
METER_CONDITION is an unsupported type, check if it needs cleaning or further analysisUnsupported
PERMIT_RATE is an unsupported type, check if it needs cleaning or further analysisUnsupported
PURCHASED_DATE is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-09-04 00:49:42.894696
Analysis finished2024-09-04 00:56:21.520346
Duration6 minutes and 38.63 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

_id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct6955
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3478
Minimum1
Maximum6955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:21.567183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile348.7
Q11739.5
median3478
Q35216.5
95-th percentile6607.3
Maximum6955
Range6954
Interquartile range (IQR)3477

Descriptive statistics

Standard deviation2007.8799
Coefficient of variation (CV)0.57730877
Kurtosis-1.2
Mean3478
Median Absolute Deviation (MAD)1739
Skewness0
Sum24189490
Variance4031581.7
MonotonicityStrictly increasing
2024-09-03T20:56:21.641257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
4633 1
 
< 0.1%
4644 1
 
< 0.1%
4643 1
 
< 0.1%
4642 1
 
< 0.1%
4641 1
 
< 0.1%
4640 1
 
< 0.1%
4639 1
 
< 0.1%
4638 1
 
< 0.1%
4637 1
 
< 0.1%
Other values (6945) 6945
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
6955 1
< 0.1%
6954 1
< 0.1%
6953 1
< 0.1%
6952 1
< 0.1%
6951 1
< 0.1%
6950 1
< 0.1%
6949 1
< 0.1%
6948 1
< 0.1%
6947 1
< 0.1%
6946 1
< 0.1%

X
Real number (ℝ)

HIGH CORRELATION 

Distinct6952
Distinct (%)> 99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean769552.06
Minimum749959.38
Maximum779854.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:21.714913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum749959.38
5-th percentile756186.67
Q1765448.44
median771280.34
Q3773943.07
95-th percentile778577.56
Maximum779854.93
Range29895.541
Interquartile range (IQR)8494.6338

Descriptive statistics

Standard deviation6430.7536
Coefficient of variation (CV)0.0083564894
Kurtosis0.41525169
Mean769552.06
Median Absolute Deviation (MAD)4073.9397
Skewness-0.84690137
Sum5.351465 × 109
Variance41354592
MonotonicityNot monotonic
2024-09-03T20:56:21.785670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
772190.4946 2
 
< 0.1%
772152.3096 2
 
< 0.1%
771671.6128 1
 
< 0.1%
765212.5443 1
 
< 0.1%
765633.1091 1
 
< 0.1%
765661.8472 1
 
< 0.1%
765365.1697 1
 
< 0.1%
765346.1927 1
 
< 0.1%
765326.9453 1
 
< 0.1%
765307.9683 1
 
< 0.1%
Other values (6942) 6942
99.8%
ValueCountFrequency (%)
749959.3842 1
< 0.1%
749972.1506 1
< 0.1%
749985.1876 1
< 0.1%
749997.9556 1
< 0.1%
750010.9924 1
< 0.1%
750023.7591 1
< 0.1%
750036.7958 1
< 0.1%
750049.5641 1
< 0.1%
750062.6009 1
< 0.1%
750075.3676 1
< 0.1%
ValueCountFrequency (%)
779854.9255 1
< 0.1%
779840.3513 1
< 0.1%
779837.5679 1
< 0.1%
779827.4498 1
< 0.1%
779821.5522 1
< 0.1%
779814.0154 1
< 0.1%
779804.9997 1
< 0.1%
779799.5135 1
< 0.1%
779787.9085 1
< 0.1%
779786.4138 1
< 0.1%

Y
Real number (ℝ)

HIGH CORRELATION 

Distinct6953
Distinct (%)> 99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2952463.2
Minimum2946649.9
Maximum2962597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:21.854710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2946649.9
5-th percentile2947989.2
Q12950978
median2952683.5
Q32953629.4
95-th percentile2956915.8
Maximum2962597
Range15947.114
Interquartile range (IQR)2651.4333

Descriptive statistics

Standard deviation2615.011
Coefficient of variation (CV)0.00088570486
Kurtosis1.1639383
Mean2952463.2
Median Absolute Deviation (MAD)1305.0171
Skewness0.37404136
Sum2.0531429 × 1010
Variance6838282.6
MonotonicityNot monotonic
2024-09-03T20:56:21.929302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2956423.307 2
 
< 0.1%
2953700.433 1
 
< 0.1%
2948789.663 1
 
< 0.1%
2948605.298 1
 
< 0.1%
2948588.305 1
 
< 0.1%
2948571.678 1
 
< 0.1%
2948703.287 1
 
< 0.1%
2948714.13 1
 
< 0.1%
2948724.972 1
 
< 0.1%
2948735.814 1
 
< 0.1%
Other values (6943) 6943
99.8%
ValueCountFrequency (%)
2946649.905 1
< 0.1%
2946662.541 1
< 0.1%
2946674.813 1
< 0.1%
2946687.448 1
< 0.1%
2946699.72 1
< 0.1%
2946712.356 1
< 0.1%
2946724.627 1
< 0.1%
2946737.264 1
< 0.1%
2946749.535 1
< 0.1%
2946762.171 1
< 0.1%
ValueCountFrequency (%)
2962597.02 1
< 0.1%
2962571.995 1
< 0.1%
2962560.75 1
< 0.1%
2962539.329 1
< 0.1%
2962526.987 1
< 0.1%
2962496.155 1
< 0.1%
2962487.806 1
< 0.1%
2962455.882 1
< 0.1%
2962447.18 1
< 0.1%
2962409.542 1
< 0.1%

OBJECTID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct6955
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3478
Minimum1
Maximum6955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:22.004532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile348.7
Q11739.5
median3478
Q35216.5
95-th percentile6607.3
Maximum6955
Range6954
Interquartile range (IQR)3477

Descriptive statistics

Standard deviation2007.8799
Coefficient of variation (CV)0.57730877
Kurtosis-1.2
Mean3478
Median Absolute Deviation (MAD)1739
Skewness0
Sum24189490
Variance4031581.7
MonotonicityStrictly increasing
2024-09-03T20:56:22.079201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
4633 1
 
< 0.1%
4644 1
 
< 0.1%
4643 1
 
< 0.1%
4642 1
 
< 0.1%
4641 1
 
< 0.1%
4640 1
 
< 0.1%
4639 1
 
< 0.1%
4638 1
 
< 0.1%
4637 1
 
< 0.1%
Other values (6945) 6945
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
6955 1
< 0.1%
6954 1
< 0.1%
6953 1
< 0.1%
6952 1
< 0.1%
6951 1
< 0.1%
6950 1
< 0.1%
6949 1
< 0.1%
6948 1
< 0.1%
6947 1
< 0.1%
6946 1
< 0.1%

METER_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct124
Distinct (%)100.0%
Missing6831
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean450183.44
Minimum450001
Maximum450703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:22.151538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum450001
5-th percentile450007.15
Q1450033.75
median450064.5
Q3450502.25
95-th percentile450603.85
Maximum450703
Range702
Interquartile range (IQR)468.5

Descriptive statistics

Standard deviation229.3121
Coefficient of variation (CV)0.00050937479
Kurtosis-0.55710269
Mean450183.44
Median Absolute Deviation (MAD)35
Skewness1.1168782
Sum55822747
Variance52584.037
MonotonicityStrictly increasing
2024-09-03T20:56:22.226500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450083 1
 
< 0.1%
450502 1
 
< 0.1%
450501 1
 
< 0.1%
450101 1
 
< 0.1%
450100 1
 
< 0.1%
450099 1
 
< 0.1%
450098 1
 
< 0.1%
450097 1
 
< 0.1%
450095 1
 
< 0.1%
450093 1
 
< 0.1%
Other values (114) 114
 
1.6%
(Missing) 6831
98.2%
ValueCountFrequency (%)
450001 1
< 0.1%
450002 1
< 0.1%
450003 1
< 0.1%
450004 1
< 0.1%
450005 1
< 0.1%
450006 1
< 0.1%
450007 1
< 0.1%
450008 1
< 0.1%
450009 1
< 0.1%
450010 1
< 0.1%
ValueCountFrequency (%)
450703 1
< 0.1%
450702 1
< 0.1%
450701 1
< 0.1%
450700 1
< 0.1%
450606 1
< 0.1%
450605 1
< 0.1%
450604 1
< 0.1%
450603 1
< 0.1%
450602 1
< 0.1%
450601 1
< 0.1%

VENDOR
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
IPS
6830 
Parkeon
 
124

Length

Max length7
Median length3
Mean length3.0713259
Min length3

Characters and Unicode

Total characters21358
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParkeon
2nd rowParkeon
3rd rowParkeon
4th rowParkeon
5th rowParkeon

Common Values

ValueCountFrequency (%)
IPS 6830
98.2%
Parkeon 124
 
1.8%
(Missing) 1
 
< 0.1%

Length

2024-09-03T20:56:22.304010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:22.365260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ips 6830
98.2%
parkeon 124
 
1.8%

Most occurring characters

ValueCountFrequency (%)
P 6954
32.6%
I 6830
32.0%
S 6830
32.0%
a 124
 
0.6%
r 124
 
0.6%
k 124
 
0.6%
e 124
 
0.6%
o 124
 
0.6%
n 124
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20614
96.5%
Lowercase Letter 744
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 124
16.7%
r 124
16.7%
k 124
16.7%
e 124
16.7%
o 124
16.7%
n 124
16.7%
Uppercase Letter
ValueCountFrequency (%)
P 6954
33.7%
I 6830
33.1%
S 6830
33.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 21358
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 6954
32.6%
I 6830
32.0%
S 6830
32.0%
a 124
 
0.6%
r 124
 
0.6%
k 124
 
0.6%
e 124
 
0.6%
o 124
 
0.6%
n 124
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 6954
32.6%
I 6830
32.0%
S 6830
32.0%
a 124
 
0.6%
r 124
 
0.6%
k 124
 
0.6%
e 124
 
0.6%
o 124
 
0.6%
n 124
 
0.6%

PAY_POLICY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct28
Distinct (%)0.4%
Missing2
Missing (%)< 0.1%
Memory size54.5 KiB
08:00AM-08:00PM MON-SAT $0.25 120
2725 
08:00AM-06:00PM MON-SAT $0.25 120
2668 
08:00AM-06:00PM MON-SAT $0.25 240
855 
08:00AM-06:00PM MON-FRI $0.25 120
 
253
08:00AM-08:00PM MON-SAT $0.25 240
 
81
Other values (23)
371 

Length

Max length134
Median length33
Mean length34.155185
Min length29

Characters and Unicode

Total characters237481
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row08:00AM-08:00PM MON-SAT $0.25 120
2nd row08:00AM-08:00PM MON-SAT $0.25 120
3rd row08:00AM-08:00PM MON-SAT $0.25 120
4th row08:00AM-08:00PM MON-SAT $0.25 120
5th row08:00AM-08:00PM MON-SAT $0.25 120

Common Values

ValueCountFrequency (%)
08:00AM-08:00PM MON-SAT $0.25 120 2725
39.2%
08:00AM-06:00PM MON-SAT $0.25 120 2668
38.4%
08:00AM-06:00PM MON-SAT $0.25 240 855
 
12.3%
08:00AM-06:00PM MON-FRI $0.25 120 253
 
3.6%
08:00AM-08:00PM MON-SAT $0.25 240 81
 
1.2%
10:00AM-06:00PM MON-SAT $0.25 120 52
 
0.7%
08:00AM-04:00PM MON-FRI $0.25 120, 08:00AM-06:00PM SAT $0.25 120 48
 
0.7%
08:00AM-08:00PM MON-SAT $0.25 720 39
 
0.6%
08:00AM-08:00PM SAT $0.25 120, 09:30AM-04:00PM MON-FRI $0.25 120, 06:00PM-08:00PM MON-FRI $0.25 120 35
 
0.5%
09:00AM-05:00PM MON-SAT $0.25 120 32
 
0.5%
Other values (18) 165
 
2.4%

Length

2024-09-03T20:56:22.419010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.25 7188
24.9%
mon-sat 6502
22.6%
120 6222
21.6%
08:00am-06:00pm 3889
13.5%
08:00am-08:00pm 2946
10.2%
240 936
 
3.2%
mon-fri 507
 
1.8%
sat 195
 
0.7%
08:00am-04:00pm 71
 
0.2%
09:30am-04:00pm 65
 
0.2%
Other values (17) 299
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 57492
24.2%
21867
 
9.2%
M 21419
 
9.0%
: 14410
 
6.1%
2 14394
 
6.1%
- 14215
 
6.0%
A 13848
 
5.8%
8 9965
 
4.2%
P 7260
 
3.1%
5 7245
 
3.1%
Other values (17) 55366
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100862
42.5%
Uppercase Letter 71465
30.1%
Space Separator 21867
 
9.2%
Other Punctuation 21867
 
9.2%
Dash Punctuation 14215
 
6.0%
Currency Symbol 7205
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 21419
30.0%
A 13848
19.4%
P 7260
 
10.2%
N 7010
 
9.8%
O 7009
 
9.8%
S 6699
 
9.4%
T 6698
 
9.4%
F 507
 
0.7%
R 507
 
0.7%
I 507
 
0.7%
Decimal Number
ValueCountFrequency (%)
0 57492
57.0%
2 14394
 
14.3%
8 9965
 
9.9%
5 7245
 
7.2%
1 6360
 
6.3%
6 4020
 
4.0%
4 1085
 
1.1%
9 139
 
0.1%
3 123
 
0.1%
7 39
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
: 14410
65.9%
. 7205
32.9%
, 252
 
1.2%
Space Separator
ValueCountFrequency (%)
21867
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14215
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 7205
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 166016
69.9%
Latin 71465
30.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 57492
34.6%
21867
 
13.2%
: 14410
 
8.7%
2 14394
 
8.7%
- 14215
 
8.6%
8 9965
 
6.0%
5 7245
 
4.4%
$ 7205
 
4.3%
. 7205
 
4.3%
1 6360
 
3.8%
Other values (6) 5658
 
3.4%
Latin
ValueCountFrequency (%)
M 21419
30.0%
A 13848
19.4%
P 7260
 
10.2%
N 7010
 
9.8%
O 7009
 
9.8%
S 6699
 
9.4%
T 6698
 
9.4%
F 507
 
0.7%
R 507
 
0.7%
I 507
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 237481
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57492
24.2%
21867
 
9.2%
M 21419
 
9.0%
: 14410
 
6.1%
2 14394
 
6.1%
- 14215
 
6.0%
A 13848
 
5.8%
8 9965
 
4.2%
P 7260
 
3.1%
5 7245
 
3.1%
Other values (17) 55366
23.3%

PRE_PAY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

PARK_NO_PAY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size54.5 KiB
00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SAT
3523 
00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SAT
2877 
00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-FRI, 00:00AM-24:00AM SAT, 06:00PM-24:00AM MON-FRI
 
253
00:00AM-24:00AM SUN, 00:00AM-10:00AM MON-SAT, 06:00PM-24:00AM MON-SAT
 
52
00:00AM-24:00AM SUN, 00:00AM-09:30AM MON-FRI, 00:00AM-08:00AM SAT, 08:00PM-24:00AM MON-SAT
 
49
Other values (15)
 
199

Length

Max length111
Median length69
Mean length70.386452
Min length48

Characters and Unicode

Total characters489397
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SAT
2nd row00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SAT
3rd row00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SAT
4th row00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SAT
5th row00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SAT

Common Values

ValueCountFrequency (%)
00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SAT 3523
50.7%
00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SAT 2877
41.4%
00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-FRI, 00:00AM-24:00AM SAT, 06:00PM-24:00AM MON-FRI 253
 
3.6%
00:00AM-24:00AM SUN, 00:00AM-10:00AM MON-SAT, 06:00PM-24:00AM MON-SAT 52
 
0.7%
00:00AM-24:00AM SUN, 00:00AM-09:30AM MON-FRI, 00:00AM-08:00AM SAT, 08:00PM-24:00AM MON-SAT 49
 
0.7%
00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 04:00PM-24:00AM MON-FRI, 06:00PM-24:00AM SAT 48
 
0.7%
00:00AM-24:00AM SUN, 00:00AM-09:00AM MON-SAT, 05:00PM-24:00AM MON-SAT 32
 
0.5%
00:00AM-24:00AM SUN, 00:00AM-09:30AM MON-FRI, 00:00AM-08:00AM SAT, 04:00PM-24:00AM MON-FRI, 06:00PM-24:00AM SAT 30
 
0.4%
00:00AM-24:00AM SUN, 00:00AM-11:00AM MON-SAT, 08:00PM-24:00AM MON-SAT 28
 
0.4%
00:00AM-24:00AM SUN, 00:00AM-09:30AM MON-FRI, 00:00AM-08:00AM SAT, 06:00PM-24:00AM MON-SAT 24
 
0.3%
Other values (10) 37
 
0.5%

Length

2024-09-03T20:56:22.482340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mon-sat 13178
30.9%
00:00am-24:00am 7207
16.9%
sun 6947
16.3%
00:00am-08:00am 6840
16.0%
06:00pm-24:00am 3942
 
9.2%
08:00pm-24:00am 2975
 
7.0%
mon-fri 716
 
1.7%
sat 471
 
1.1%
00:00am-09:30am 107
 
0.3%
04:00pm-24:00am 86
 
0.2%
Other values (8) 169
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 127793
26.1%
M 56532
11.6%
A 49241
 
10.1%
: 42638
 
8.7%
35685
 
7.3%
- 35220
 
7.2%
N 20848
 
4.3%
S 20605
 
4.2%
, 14366
 
2.9%
4 14341
 
2.9%
Other values (14) 72128
14.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 190936
39.0%
Decimal Number 170552
34.8%
Other Punctuation 57004
 
11.6%
Space Separator 35685
 
7.3%
Dash Punctuation 35220
 
7.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 56532
29.6%
A 49241
25.8%
N 20848
 
10.9%
S 20605
 
10.8%
O 13894
 
7.3%
T 13651
 
7.1%
P 7048
 
3.7%
U 6954
 
3.6%
F 721
 
0.4%
R 721
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 127793
74.9%
4 14341
 
8.4%
2 14254
 
8.4%
8 9815
 
5.8%
6 3942
 
2.3%
9 139
 
0.1%
3 115
 
0.1%
1 113
 
0.1%
5 40
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
: 42638
74.8%
, 14366
 
25.2%
Space Separator
ValueCountFrequency (%)
35685
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 298461
61.0%
Latin 190936
39.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127793
42.8%
: 42638
 
14.3%
35685
 
12.0%
- 35220
 
11.8%
, 14366
 
4.8%
4 14341
 
4.8%
2 14254
 
4.8%
8 9815
 
3.3%
6 3942
 
1.3%
9 139
 
< 0.1%
Other values (3) 268
 
0.1%
Latin
ValueCountFrequency (%)
M 56532
29.6%
A 49241
25.8%
N 20848
 
10.9%
S 20605
 
10.8%
O 13894
 
7.3%
T 13651
 
7.1%
P 7048
 
3.7%
U 6954
 
3.6%
F 721
 
0.4%
R 721
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 489397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127793
26.1%
M 56532
11.6%
A 49241
 
10.1%
: 42638
 
8.7%
35685
 
7.3%
- 35220
 
7.2%
N 20848
 
4.3%
S 20605
 
4.2%
, 14366
 
2.9%
4 14341
 
2.9%
Other values (14) 72128
14.7%

GREEN_DOME
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

TOW_AWAY
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing6901
Missing (%)99.2%
Memory size54.5 KiB
04:00PM-06:00PM MON-FRI
54 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters1242
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04:00PM-06:00PM MON-FRI
2nd row04:00PM-06:00PM MON-FRI
3rd row04:00PM-06:00PM MON-FRI
4th row04:00PM-06:00PM MON-FRI
5th row04:00PM-06:00PM MON-FRI

Common Values

ValueCountFrequency (%)
04:00PM-06:00PM MON-FRI 54
 
0.8%
(Missing) 6901
99.2%

Length

2024-09-03T20:56:22.542001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:22.586908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
04:00pm-06:00pm 54
50.0%
mon-fri 54
50.0%

Most occurring characters

ValueCountFrequency (%)
0 324
26.1%
M 162
13.0%
: 108
 
8.7%
P 108
 
8.7%
- 108
 
8.7%
4 54
 
4.3%
6 54
 
4.3%
54
 
4.3%
O 54
 
4.3%
N 54
 
4.3%
Other values (3) 162
13.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 540
43.5%
Decimal Number 432
34.8%
Other Punctuation 108
 
8.7%
Dash Punctuation 108
 
8.7%
Space Separator 54
 
4.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 162
30.0%
P 108
20.0%
O 54
 
10.0%
N 54
 
10.0%
F 54
 
10.0%
R 54
 
10.0%
I 54
 
10.0%
Decimal Number
ValueCountFrequency (%)
0 324
75.0%
4 54
 
12.5%
6 54
 
12.5%
Other Punctuation
ValueCountFrequency (%)
: 108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702
56.5%
Latin 540
43.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 162
30.0%
P 108
20.0%
O 54
 
10.0%
N 54
 
10.0%
F 54
 
10.0%
R 54
 
10.0%
I 54
 
10.0%
Common
ValueCountFrequency (%)
0 324
46.2%
: 108
 
15.4%
- 108
 
15.4%
4 54
 
7.7%
6 54
 
7.7%
54
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 324
26.1%
M 162
13.0%
: 108
 
8.7%
P 108
 
8.7%
- 108
 
8.7%
4 54
 
4.3%
6 54
 
4.3%
54
 
4.3%
O 54
 
4.3%
N 54
 
4.3%
Other values (3) 162
13.0%

STREET_CLEANING
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

DIR
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size54.5 KiB
W
1827 
S
1731 
N
1722 
E
1671 
s
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6952
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowN
2nd rowS
3rd rowN
4th rowS
5th rowN

Common Values

ValueCountFrequency (%)
W 1827
26.3%
S 1731
24.9%
N 1722
24.8%
E 1671
24.0%
s 1
 
< 0.1%
(Missing) 3
 
< 0.1%

Length

2024-09-03T20:56:22.637307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:22.692632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
w 1827
26.3%
s 1732
24.9%
n 1722
24.8%
e 1671
24.0%

Most occurring characters

ValueCountFrequency (%)
W 1827
26.3%
S 1731
24.9%
N 1722
24.8%
E 1671
24.0%
s 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6951
> 99.9%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 1827
26.3%
S 1731
24.9%
N 1722
24.8%
E 1671
24.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6952
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 1827
26.3%
S 1731
24.9%
N 1722
24.8%
E 1671
24.0%
s 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 1827
26.3%
S 1731
24.9%
N 1722
24.8%
E 1671
24.0%
s 1
 
< 0.1%

BLK_NO
Text

Distinct281
Distinct (%)4.0%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
2024-09-03T20:56:22.910592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.9791487
Min length1

Characters and Unicode

Total characters27671
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st rowARLI
2nd rowARLI
3rd rowARLI
4th rowARLI
5th rowARLI
ValueCountFrequency (%)
comm 311
 
4.1%
mass 227
 
3.0%
char 206
 
2.7%
e 202
 
2.7%
beac 169
 
2.2%
alba 161
 
2.1%
hunt 152
 
2.0%
st 151
 
2.0%
berk 140
 
1.9%
marl 126
 
1.7%
Other values (265) 5676
75.5%
2024-09-03T20:56:23.213100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3350
 
12.1%
R 2660
 
9.6%
E 2154
 
7.8%
M 1719
 
6.2%
C 1676
 
6.1%
S 1642
 
5.9%
L 1552
 
5.6%
B 1545
 
5.6%
O 1450
 
5.2%
N 1353
 
4.9%
Other values (17) 8570
31.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27030
97.7%
Space Separator 567
 
2.0%
Other Punctuation 74
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3350
12.4%
R 2660
 
9.8%
E 2154
 
8.0%
M 1719
 
6.4%
C 1676
 
6.2%
S 1642
 
6.1%
L 1552
 
5.7%
B 1545
 
5.7%
O 1450
 
5.4%
N 1353
 
5.0%
Other values (15) 7929
29.3%
Space Separator
ValueCountFrequency (%)
567
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27030
97.7%
Common 641
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3350
12.4%
R 2660
 
9.8%
E 2154
 
8.0%
M 1719
 
6.4%
C 1676
 
6.2%
S 1642
 
6.1%
L 1552
 
5.7%
B 1545
 
5.7%
O 1450
 
5.4%
N 1353
 
5.0%
Other values (15) 7929
29.3%
Common
ValueCountFrequency (%)
567
88.5%
/ 74
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3350
 
12.1%
R 2660
 
9.6%
E 2154
 
7.8%
M 1719
 
6.2%
C 1676
 
6.1%
S 1642
 
5.9%
L 1552
 
5.6%
B 1545
 
5.6%
O 1450
 
5.2%
N 1353
 
4.9%
Other values (17) 8570
31.0%

STREET
Text

Distinct188
Distinct (%)2.7%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
2024-09-03T20:56:23.370891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length11.270779
Min length7

Characters and Unicode

Total characters78377
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st rowNEWBURY ST A-B
2nd rowNEWBURY ST A-B
3rd rowNEWBURY ST A-B
4th rowNEWBURY ST A-B
5th rowNEWBURY ST A-B
ValueCountFrequency (%)
st 4364
29.5%
av 1626
 
11.0%
commonwealth 721
 
4.9%
beacon 482
 
3.3%
rd 385
 
2.6%
tremont 247
 
1.7%
washington 245
 
1.7%
street 237
 
1.6%
harrison 221
 
1.5%
charles 214
 
1.4%
Other values (191) 6060
40.9%
2024-09-03T20:56:23.600755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 9023
11.5%
7848
 
10.0%
S 7553
 
9.6%
A 6715
 
8.6%
E 6244
 
8.0%
N 5174
 
6.6%
O 5018
 
6.4%
R 4487
 
5.7%
L 2966
 
3.8%
M 2903
 
3.7%
Other values (17) 20446
26.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 70408
89.8%
Space Separator 7848
 
10.0%
Dash Punctuation 121
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 9023
12.8%
S 7553
10.7%
A 6715
 
9.5%
E 6244
 
8.9%
N 5174
 
7.3%
O 5018
 
7.1%
R 4487
 
6.4%
L 2966
 
4.2%
M 2903
 
4.1%
H 2860
 
4.1%
Other values (15) 17465
24.8%
Space Separator
ValueCountFrequency (%)
7848
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70408
89.8%
Common 7969
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 9023
12.8%
S 7553
10.7%
A 6715
 
9.5%
E 6244
 
8.9%
N 5174
 
7.3%
O 5018
 
7.1%
R 4487
 
6.4%
L 2966
 
4.2%
M 2903
 
4.1%
H 2860
 
4.1%
Other values (15) 17465
24.8%
Common
ValueCountFrequency (%)
7848
98.5%
- 121
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 9023
11.5%
7848
 
10.0%
S 7553
 
9.6%
A 6715
 
8.6%
E 6244
 
8.0%
N 5174
 
6.6%
O 5018
 
6.4%
R 4487
 
5.7%
L 2966
 
3.8%
M 2903
 
3.7%
Other values (17) 20446
26.1%

LOCK_
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
1
6940 
0
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6954
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6940
99.8%
0 14
 
0.2%
(Missing) 1
 
< 0.1%

Length

2024-09-03T20:56:23.683822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:23.763789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6940
99.8%
0 14
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 6940
99.8%
0 14
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6954
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6940
99.8%
0 14
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 6954
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6940
99.8%
0 14
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6954
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6940
99.8%
0 14
 
0.2%

LOCK__
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

LONGITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct6620
Distinct (%)95.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-71.08055
Minimum-71.153092
Maximum-71.042454
Zeros0
Zeros (%)0.0%
Negative6954
Negative (%)> 99.9%
Memory size54.5 KiB
2024-09-03T20:56:23.822013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-71.153092
5-th percentile-71.129991
Q1-71.095743
median-71.074181
Q3-71.064317
95-th percentile-71.047194
Maximum-71.042454
Range0.110638
Interquartile range (IQR)0.0314265

Descriptive statistics

Standard deviation0.023803474
Coefficient of variation (CV)-0.00033488028
Kurtosis0.41455712
Mean-71.08055
Median Absolute Deviation (MAD)0.01508
Skewness-0.8459397
Sum-494294.14
Variance0.00056660539
MonotonicityNot monotonic
2024-09-03T20:56:23.893996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-71.071976 3
 
< 0.1%
-71.070886 3
 
< 0.1%
-71.096456 3
 
< 0.1%
-71.074077 3
 
< 0.1%
-71.094054 3
 
< 0.1%
-71.072068 3
 
< 0.1%
-71.059879 3
 
< 0.1%
-71.057288 3
 
< 0.1%
-71.07353 3
 
< 0.1%
-71.07081 3
 
< 0.1%
Other values (6610) 6924
99.6%
ValueCountFrequency (%)
-71.153092 1
< 0.1%
-71.153045 1
< 0.1%
-71.152997 1
< 0.1%
-71.15295 1
< 0.1%
-71.152902 1
< 0.1%
-71.152855 1
< 0.1%
-71.152807 1
< 0.1%
-71.15276 1
< 0.1%
-71.152712 1
< 0.1%
-71.152665 1
< 0.1%
ValueCountFrequency (%)
-71.042454 1
< 0.1%
-71.042511 1
< 0.1%
-71.042518 1
< 0.1%
-71.042559 1
< 0.1%
-71.042577 1
< 0.1%
-71.042609 1
< 0.1%
-71.042638 1
< 0.1%
-71.042663 1
< 0.1%
-71.042701 1
< 0.1%
-71.042712 1
< 0.1%

LATITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct5791
Distinct (%)83.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean42.348942
Minimum42.333067
Maximum42.376649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:23.962857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum42.333067
5-th percentile42.336721
Q142.344883
median42.349549
Q342.352144
95-th percentile42.361097
Maximum42.376649
Range0.043582
Interquartile range (IQR)0.00726075

Descriptive statistics

Standard deviation0.0071495998
Coefficient of variation (CV)0.00016882593
Kurtosis1.1562212
Mean42.348942
Median Absolute Deviation (MAD)0.0035825
Skewness0.37039851
Sum294494.55
Variance5.1116777 × 10-5
MonotonicityNot monotonic
2024-09-03T20:56:24.027638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.347454 6
 
0.1%
42.347456 6
 
0.1%
42.347455 6
 
0.1%
42.348014 5
 
0.1%
42.349797 5
 
0.1%
42.348007 5
 
0.1%
42.350135 5
 
0.1%
42.353366 5
 
0.1%
42.348015 5
 
0.1%
42.350428 4
 
0.1%
Other values (5781) 6902
99.2%
ValueCountFrequency (%)
42.333067 1
< 0.1%
42.333102 1
< 0.1%
42.333136 1
< 0.1%
42.333171 1
< 0.1%
42.333205 1
< 0.1%
42.33324 1
< 0.1%
42.333274 1
< 0.1%
42.333309 1
< 0.1%
42.333343 1
< 0.1%
42.333378 1
< 0.1%
ValueCountFrequency (%)
42.376649 1
< 0.1%
42.37658 1
< 0.1%
42.376549 1
< 0.1%
42.37649 1
< 0.1%
42.376456 1
< 0.1%
42.376371 1
< 0.1%
42.376348 1
< 0.1%
42.37626 1
< 0.1%
42.376236 1
< 0.1%
42.376133 1
< 0.1%

TRAVEL_DIRECTION
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing6953
Missing (%)> 99.9%
Memory size54.5 KiB
2024-09-03T20:56:24.063457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
ValueCountFrequency (%)
s 2
100.0%
2024-09-03T20:56:24.255854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 2
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 2
100.0%

FROM_INTERSECTION
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

TO_INTERSECTION
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

SPACE_NUMBER
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct124
Distinct (%)100.0%
Missing6831
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean183.44355
Minimum1
Maximum703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:24.317622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.15
Q133.75
median64.5
Q3502.25
95-th percentile603.85
Maximum703
Range702
Interquartile range (IQR)468.5

Descriptive statistics

Standard deviation229.3121
Coefficient of variation (CV)1.2500418
Kurtosis-0.55710269
Mean183.44355
Median Absolute Deviation (MAD)35
Skewness1.1168782
Sum22747
Variance52584.037
MonotonicityStrictly increasing
2024-09-03T20:56:24.387552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 1
 
< 0.1%
502 1
 
< 0.1%
501 1
 
< 0.1%
101 1
 
< 0.1%
100 1
 
< 0.1%
99 1
 
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
Other values (114) 114
 
1.6%
(Missing) 6831
98.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
703 1
< 0.1%
702 1
< 0.1%
701 1
< 0.1%
700 1
< 0.1%
606 1
< 0.1%
605 1
< 0.1%
604 1
< 0.1%
603 1
< 0.1%
602 1
< 0.1%
601 1
< 0.1%

NUMBEROFSPACES
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
1
6954 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6954
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6954
> 99.9%
(Missing) 1
 
< 0.1%

Length

2024-09-03T20:56:24.452577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:24.497658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6954
100.0%

Most occurring characters

ValueCountFrequency (%)
1 6954
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6954
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6954
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6954
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6954
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6954
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6954
100.0%

METER_TYPE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
SINGLE-SPACE
6830 
MULTI-SPACE STALL
 
124

Length

Max length17
Median length12
Mean length12.089157
Min length12

Characters and Unicode

Total characters84068
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMULTI-SPACE STALL
2nd rowMULTI-SPACE STALL
3rd rowMULTI-SPACE STALL
4th rowMULTI-SPACE STALL
5th rowMULTI-SPACE STALL

Common Values

ValueCountFrequency (%)
SINGLE-SPACE 6830
98.2%
MULTI-SPACE STALL 124
 
1.8%
(Missing) 1
 
< 0.1%

Length

2024-09-03T20:56:24.550311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:24.618539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
single-space 6830
96.5%
multi-space 124
 
1.8%
stall 124
 
1.8%

Most occurring characters

ValueCountFrequency (%)
S 13908
16.5%
E 13784
16.4%
L 7202
8.6%
A 7078
8.4%
I 6954
8.3%
- 6954
8.3%
P 6954
8.3%
C 6954
8.3%
N 6830
8.1%
G 6830
8.1%
Other values (4) 620
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 76990
91.6%
Dash Punctuation 6954
 
8.3%
Space Separator 124
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 13908
18.1%
E 13784
17.9%
L 7202
9.4%
A 7078
9.2%
I 6954
9.0%
P 6954
9.0%
C 6954
9.0%
N 6830
8.9%
G 6830
8.9%
T 248
 
0.3%
Other values (2) 248
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 6954
100.0%
Space Separator
ValueCountFrequency (%)
124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76990
91.6%
Common 7078
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 13908
18.1%
E 13784
17.9%
L 7202
9.4%
A 7078
9.2%
I 6954
9.0%
P 6954
9.0%
C 6954
9.0%
N 6830
8.9%
G 6830
8.9%
T 248
 
0.3%
Other values (2) 248
 
0.3%
Common
ValueCountFrequency (%)
- 6954
98.2%
124
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 13908
16.5%
E 13784
16.4%
L 7202
8.6%
A 7078
8.4%
I 6954
8.3%
- 6954
8.3%
P 6954
8.3%
C 6954
8.3%
N 6830
8.1%
G 6830
8.1%
Other values (4) 620
 
0.7%

HAS_SENSOR
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size13.7 KiB
False
6954 
(Missing)
 
1
ValueCountFrequency (%)
False 6954
> 99.9%
(Missing) 1
 
< 0.1%
2024-09-03T20:56:24.694131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

G_DISTRICT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing15
Missing (%)0.2%
Memory size54.5 KiB
DISTRICT 0
6928 
DISTRICT 1
 
12

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters69400
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDISTRICT 0
2nd rowDISTRICT 0
3rd rowDISTRICT 0
4th rowDISTRICT 0
5th rowDISTRICT 0

Common Values

ValueCountFrequency (%)
DISTRICT 0 6928
99.6%
DISTRICT 1 12
 
0.2%
(Missing) 15
 
0.2%

Length

2024-09-03T20:56:24.762997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:24.822790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
district 6940
50.0%
0 6928
49.9%
1 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 13880
20.0%
T 13880
20.0%
D 6940
10.0%
S 6940
10.0%
R 6940
10.0%
C 6940
10.0%
6940
10.0%
0 6928
10.0%
1 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 55520
80.0%
Space Separator 6940
 
10.0%
Decimal Number 6940
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 13880
25.0%
T 13880
25.0%
D 6940
12.5%
S 6940
12.5%
R 6940
12.5%
C 6940
12.5%
Decimal Number
ValueCountFrequency (%)
0 6928
99.8%
1 12
 
0.2%
Space Separator
ValueCountFrequency (%)
6940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55520
80.0%
Common 13880
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 13880
25.0%
T 13880
25.0%
D 6940
12.5%
S 6940
12.5%
R 6940
12.5%
C 6940
12.5%
Common
ValueCountFrequency (%)
6940
50.0%
0 6928
49.9%
1 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 13880
20.0%
T 13880
20.0%
D 6940
10.0%
S 6940
10.0%
R 6940
10.0%
C 6940
10.0%
6940
10.0%
0 6928
10.0%
1 12
 
< 0.1%

G_PASSPORT_ZONES
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct587
Distinct (%)100.0%
Missing6368
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean469.6644
Minimum1
Maximum1005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:24.894688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile67.3
Q1257.5
median455
Q3689
95-th percentile949.7
Maximum1005
Range1004
Interquartile range (IQR)431.5

Descriptive statistics

Standard deviation265.23268
Coefficient of variation (CV)0.56472809
Kurtosis-0.94415444
Mean469.6644
Median Absolute Deviation (MAD)214
Skewness0.16210873
Sum275693
Variance70348.374
MonotonicityNot monotonic
2024-09-03T20:56:25.020061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
575 1
 
< 0.1%
544 1
 
< 0.1%
545 1
 
< 0.1%
546 1
 
< 0.1%
548 1
 
< 0.1%
549 1
 
< 0.1%
550 1
 
< 0.1%
551 1
 
< 0.1%
552 1
 
< 0.1%
554 1
 
< 0.1%
Other values (577) 577
 
8.3%
(Missing) 6368
91.6%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
1005 1
< 0.1%
1002 1
< 0.1%
1001 1
< 0.1%
1000 1
< 0.1%
999 1
< 0.1%
995 1
< 0.1%
994 1
< 0.1%
992 1
< 0.1%
991 1
< 0.1%
990 1
< 0.1%

G_PM_ZONE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct184
Distinct (%)100.0%
Missing6771
Missing (%)97.4%
Infinite0
Infinite (%)0.0%
Mean593.97283
Minimum12
Maximum1009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2024-09-03T20:56:25.122482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile37.3
Q1260.25
median656.5
Q3906.25
95-th percentile979.55
Maximum1009
Range997
Interquartile range (IQR)646

Descriptive statistics

Standard deviation324.1902
Coefficient of variation (CV)0.54579971
Kurtosis-1.2521823
Mean593.97283
Median Absolute Deviation (MAD)257
Skewness-0.43639609
Sum109291
Variance105099.28
MonotonicityNot monotonic
2024-09-03T20:56:25.246554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
465 1
 
< 0.1%
1006 1
 
< 0.1%
400 1
 
< 0.1%
410 1
 
< 0.1%
20 1
 
< 0.1%
439 1
 
< 0.1%
442 1
 
< 0.1%
1007 1
 
< 0.1%
452 1
 
< 0.1%
454 1
 
< 0.1%
Other values (174) 174
 
2.5%
(Missing) 6771
97.4%
ValueCountFrequency (%)
12 1
< 0.1%
14 1
< 0.1%
16 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
31 1
< 0.1%
35 1
< 0.1%
37 1
< 0.1%
ValueCountFrequency (%)
1009 1
< 0.1%
1007 1
< 0.1%
1006 1
< 0.1%
998 1
< 0.1%
997 1
< 0.1%
996 1
< 0.1%
983 1
< 0.1%
982 1
< 0.1%
981 1
< 0.1%
980 1
< 0.1%

G_SUBZONE
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)0.7%
Missing15
Missing (%)0.2%
Memory size54.5 KiB
0DD
 
283
0DF
 
268
0DA
 
241
0AD
 
224
0KE
 
220
Other values (41)
5704 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20820
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0MS
2nd row0MS
3rd row0MS
4th row0MS
5th row0MS

Common Values

ValueCountFrequency (%)
0DD 283
 
4.1%
0DF 268
 
3.9%
0DA 241
 
3.5%
0AD 224
 
3.2%
0KE 220
 
3.2%
0FB 207
 
3.0%
0BD 199
 
2.9%
0ED 198
 
2.8%
0EE 191
 
2.7%
0KB 190
 
2.7%
Other values (36) 4719
67.9%

Length

2024-09-03T20:56:25.317171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0dd 283
 
4.1%
0df 268
 
3.9%
0da 241
 
3.5%
0ad 224
 
3.2%
0ke 220
 
3.2%
0fb 207
 
3.0%
0bd 199
 
2.9%
0ed 198
 
2.9%
0ee 191
 
2.8%
0kb 190
 
2.7%
Other values (36) 4719
68.0%

Most occurring characters

ValueCountFrequency (%)
0 6940
33.3%
D 2490
 
12.0%
C 2309
 
11.1%
E 1842
 
8.8%
A 1806
 
8.7%
F 1603
 
7.7%
B 1566
 
7.5%
K 863
 
4.1%
G 672
 
3.2%
H 481
 
2.3%
Other values (2) 248
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13880
66.7%
Decimal Number 6940
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 2490
17.9%
C 2309
16.6%
E 1842
13.3%
A 1806
13.0%
F 1603
11.5%
B 1566
11.3%
K 863
 
6.2%
G 672
 
4.8%
H 481
 
3.5%
M 124
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 6940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13880
66.7%
Common 6940
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 2490
17.9%
C 2309
16.6%
E 1842
13.3%
A 1806
13.0%
F 1603
11.5%
B 1566
11.3%
K 863
 
6.2%
G 672
 
4.8%
H 481
 
3.5%
M 124
 
0.9%
Common
ValueCountFrequency (%)
0 6940
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6940
33.3%
D 2490
 
12.0%
C 2309
 
11.1%
E 1842
 
8.8%
A 1806
 
8.7%
F 1603
 
7.7%
B 1566
 
7.5%
K 863
 
4.1%
G 672
 
3.2%
H 481
 
2.3%
Other values (2) 248
 
1.2%

G_ZONE
Text

Distinct52
Distinct (%)0.7%
Missing15
Missing (%)0.2%
Memory size54.5 KiB
2024-09-03T20:56:25.418272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.9992795
Min length1

Characters and Unicode

Total characters13875
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowMS
2nd rowMS
3rd rowMS
4th rowMS
5th rowMS
ValueCountFrequency (%)
dd 272
 
3.9%
df 268
 
3.9%
da 241
 
3.5%
ke 224
 
3.2%
ad 224
 
3.2%
fb 207
 
3.0%
bd 199
 
2.9%
ed 197
 
2.8%
ee 191
 
2.8%
cd 189
 
2.7%
Other values (38) 4728
68.1%
2024-09-03T20:56:25.608152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 2467
17.8%
C 2318
16.7%
E 1837
13.2%
A 1809
13.0%
F 1607
11.6%
B 1555
11.2%
K 863
 
6.2%
G 673
 
4.9%
H 481
 
3.5%
M 124
 
0.9%
Other values (3) 141
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13858
99.9%
Decimal Number 11
 
0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 2467
17.8%
C 2318
16.7%
E 1837
13.3%
A 1809
13.1%
F 1607
11.6%
B 1555
11.2%
K 863
 
6.2%
G 673
 
4.9%
H 481
 
3.5%
M 124
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13858
99.9%
Common 17
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 2467
17.8%
C 2318
16.7%
E 1837
13.3%
A 1809
13.1%
F 1607
11.6%
B 1555
11.2%
K 863
 
6.2%
G 673
 
4.9%
H 481
 
3.5%
M 124
 
0.9%
Common
ValueCountFrequency (%)
0 11
64.7%
- 6
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 2467
17.8%
C 2318
16.7%
E 1837
13.2%
A 1809
13.0%
F 1607
11.6%
B 1555
11.2%
K 863
 
6.2%
G 673
 
4.9%
H 481
 
3.5%
M 124
 
0.9%
Other values (3) 141
 
1.0%

BASE_RATE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size54.5 KiB
0.25
6947 
0.26
 
2
2
 
1
0.5
 
1
1.25
 
1

Length

Max length4
Median length4
Mean length3.9994246
Min length1

Characters and Unicode

Total characters27804
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row0.25
2nd row0.25
3rd row0.25
4th row0.25
5th row0.25

Common Values

ValueCountFrequency (%)
0.25 6947
99.9%
0.26 2
 
< 0.1%
2 1
 
< 0.1%
0.5 1
 
< 0.1%
1.25 1
 
< 0.1%
(Missing) 3
 
< 0.1%

Length

2024-09-03T20:56:25.683118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:25.745133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.25 6947
99.9%
0.26 2
 
< 0.1%
2 1
 
< 0.1%
0.5 1
 
< 0.1%
1.25 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 6951
25.0%
2 6951
25.0%
0 6950
25.0%
5 6949
25.0%
6 2
 
< 0.1%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20853
75.0%
Other Punctuation 6951
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6951
33.3%
0 6950
33.3%
5 6949
33.3%
6 2
 
< 0.1%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6951
25.0%
2 6951
25.0%
0 6950
25.0%
5 6949
25.0%
6 2
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 6951
25.0%
2 6951
25.0%
0 6950
25.0%
5 6949
25.0%
6 2
 
< 0.1%
1 1
 
< 0.1%

POLE_MOUNT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

YOKE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

HOUSING_TYPE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

HOUSING_MANUFACTURER
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

SIDEWALKGE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

COIN_SLOTLE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

METER_CONDITION
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

PERMIT_RATE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

INSTALLED_ON
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
4/1/2017 1:00:00 AM
6690 
1/1/2017 12:00:00 AM
 
264

Length

Max length20
Median length19
Mean length19.037964
Min length19

Characters and Unicode

Total characters132390
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/1/2017 12:00:00 AM
2nd row1/1/2017 12:00:00 AM
3rd row1/1/2017 12:00:00 AM
4th row1/1/2017 12:00:00 AM
5th row1/1/2017 12:00:00 AM

Common Values

ValueCountFrequency (%)
4/1/2017 1:00:00 AM 6690
96.2%
1/1/2017 12:00:00 AM 264
 
3.8%
(Missing) 1
 
< 0.1%

Length

2024-09-03T20:56:25.805264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:25.859083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
am 6954
33.3%
4/1/2017 6690
32.1%
1:00:00 6690
32.1%
1/1/2017 264
 
1.3%
12:00:00 264
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 34770
26.3%
1 21126
16.0%
/ 13908
 
10.5%
13908
 
10.5%
: 13908
 
10.5%
2 7218
 
5.5%
7 6954
 
5.3%
A 6954
 
5.3%
M 6954
 
5.3%
4 6690
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76758
58.0%
Other Punctuation 27816
 
21.0%
Space Separator 13908
 
10.5%
Uppercase Letter 13908
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34770
45.3%
1 21126
27.5%
2 7218
 
9.4%
7 6954
 
9.1%
4 6690
 
8.7%
Other Punctuation
ValueCountFrequency (%)
/ 13908
50.0%
: 13908
50.0%
Uppercase Letter
ValueCountFrequency (%)
A 6954
50.0%
M 6954
50.0%
Space Separator
ValueCountFrequency (%)
13908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118482
89.5%
Latin 13908
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34770
29.3%
1 21126
17.8%
/ 13908
 
11.7%
13908
 
11.7%
: 13908
 
11.7%
2 7218
 
6.1%
7 6954
 
5.9%
4 6690
 
5.6%
Latin
ValueCountFrequency (%)
A 6954
50.0%
M 6954
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34770
26.3%
1 21126
16.0%
/ 13908
 
10.5%
13908
 
10.5%
: 13908
 
10.5%
2 7218
 
5.5%
7 6954
 
5.3%
A 6954
 
5.3%
M 6954
 
5.3%
4 6690
 
5.1%

PURCHASED_DATE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6955
Missing (%)100.0%
Memory size54.5 KiB

METER_STATE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
ACTIVE
6954 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters41724
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACTIVE
2nd rowACTIVE
3rd rowACTIVE
4th rowACTIVE
5th rowACTIVE

Common Values

ValueCountFrequency (%)
ACTIVE 6954
> 99.9%
(Missing) 1
 
< 0.1%

Length

2024-09-03T20:56:25.914367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:25.962609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
active 6954
100.0%

Most occurring characters

ValueCountFrequency (%)
A 6954
16.7%
C 6954
16.7%
T 6954
16.7%
I 6954
16.7%
V 6954
16.7%
E 6954
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 41724
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6954
16.7%
C 6954
16.7%
T 6954
16.7%
I 6954
16.7%
V 6954
16.7%
E 6954
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 41724
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6954
16.7%
C 6954
16.7%
T 6954
16.7%
I 6954
16.7%
V 6954
16.7%
E 6954
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 6954
16.7%
C 6954
16.7%
T 6954
16.7%
I 6954
16.7%
V 6954
16.7%
E 6954
16.7%

SPACE_STATE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size54.5 KiB
ACTIVE
6954 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters41724
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACTIVE
2nd rowACTIVE
3rd rowACTIVE
4th rowACTIVE
5th rowACTIVE

Common Values

ValueCountFrequency (%)
ACTIVE 6954
> 99.9%
(Missing) 1
 
< 0.1%

Length

2024-09-03T20:56:26.014031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T20:56:26.063529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
active 6954
100.0%

Most occurring characters

ValueCountFrequency (%)
A 6954
16.7%
C 6954
16.7%
T 6954
16.7%
I 6954
16.7%
V 6954
16.7%
E 6954
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 41724
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6954
16.7%
C 6954
16.7%
T 6954
16.7%
I 6954
16.7%
V 6954
16.7%
E 6954
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 41724
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6954
16.7%
C 6954
16.7%
T 6954
16.7%
I 6954
16.7%
V 6954
16.7%
E 6954
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 6954
16.7%
C 6954
16.7%
T 6954
16.7%
I 6954
16.7%
V 6954
16.7%
E 6954
16.7%

Interactions

2024-09-03T20:56:15.985674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:49:46.784967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:24.867964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:25.750337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:25.154864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:58.686516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:02.734511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:02.593973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:00.177778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:03.586050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:16.208561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:49:46.859307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:28.688155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:29.134315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:34.314724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:58.858065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:06.125953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:05.663460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:00.340990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:04.210684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:16.844096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:49:51.038666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:36.514531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:36.702077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:47.989114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:59.307409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:13.659648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:12.777006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:00.776102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:06.297158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:17.489761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:49:55.391078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:44.565315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:44.628829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:01.660036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:59.745067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:21.487169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:20.268978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:01.212487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:08.433239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:18.113785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:14.494491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:06.956075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:06.764086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:29.519426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:00.111656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:43.695903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:42.371286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:01.697336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:10.314211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:18.333454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:14.777251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:07.411238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:07.225848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:29.893090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:00.474628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:44.118540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:42.800619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:02.072554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:10.338096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:18.927097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:19.247056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:15.011287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:15.021324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:43.209610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:00.896778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:52.041352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:50.285425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:02.475551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:12.160653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:19.643388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:23.188916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:22.312390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:22.188810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:56.019555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:01.922858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:59.135505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:56.857239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:02.883883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:14.142760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:19.857370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:23.428023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:22.728580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:22.604773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:56.396505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:02.297186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:00.009286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:57.677502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:03.362677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:14.165106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:19.921717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:50:24.500340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:51:24.668910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:52:24.527206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:53:58.170905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:54:02.333567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:01.994986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:55:59.579075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:03.382868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-03T20:56:15.787761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-03T20:56:26.114257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BASE_RATEDIRG_DISTRICTG_PASSPORT_ZONESG_PM_ZONEG_SUBZONEINSTALLED_ONLATITUDELOCK_LONGITUDEMETER_IDMETER_TYPEOBJECTIDPARK_NO_PAYPAY_POLICYSPACE_NUMBERVENDORXY_id
BASE_RATE1.0000.0000.0001.0001.0000.0310.0560.0000.0000.0001.0000.0861.0000.0000.4961.0000.0860.0240.0240.000
DIR0.0001.0000.0371.0001.0000.3510.0880.2170.0430.1261.0000.0471.0000.1510.1841.0000.0470.0170.0240.170
G_DISTRICT0.0000.0371.0001.0001.0000.1880.0000.0001.0000.2191.0000.0001.0000.0000.0001.0000.0000.0170.0120.081
G_PASSPORT_ZONES1.0001.0001.0001.0000.9821.0001.000-0.3861.000-0.680NaN1.0000.8221.0001.000NaN1.000-0.679-0.3950.822
G_PM_ZONE1.0001.0001.0000.9821.0001.0001.000-0.1441.000-0.2160.6001.000-0.1961.0001.0000.6001.000-0.216-0.148-0.196
G_SUBZONE0.0310.3510.1881.0001.0001.0000.9380.1741.0000.1181.0000.9971.0000.4080.4341.0000.9970.0810.0810.968
INSTALLED_ON0.0560.0880.0001.0001.0000.9381.0000.0000.2170.0381.0000.6751.0000.1510.2111.0000.6750.0170.0120.385
LATITUDE0.0000.2170.000-0.386-0.1440.1740.0001.0000.0000.2990.5590.000-0.3650.1730.1230.5590.0000.2971.000-0.365
LOCK_0.0000.0431.0001.0001.0001.0000.2170.0001.0000.1111.0000.0001.0000.0000.0001.0000.0000.0170.0120.035
LONGITUDE0.0000.1260.219-0.680-0.2160.1180.0380.2990.1111.0000.6990.000-0.8270.1030.1120.6990.0001.0000.313-0.827
METER_ID1.0001.0001.000NaN0.6001.0001.0000.5591.0000.6991.0001.0001.0001.0001.0001.0001.0000.6990.5591.000
METER_TYPE0.0860.0470.0001.0001.0000.9970.6750.0000.0000.0001.0001.0001.0000.1800.1841.0000.9960.0170.0120.402
OBJECTID1.0001.0001.0000.822-0.1961.0001.000-0.3651.000-0.8271.0001.0001.0001.0001.0001.0001.000-0.826-0.3771.000
PARK_NO_PAY0.0000.1510.0001.0001.0000.4080.1510.1730.0000.1031.0000.1801.0001.0000.9991.0000.1800.0520.0520.333
PAY_POLICY0.4960.1840.0001.0001.0000.4340.2110.1230.0000.1121.0000.1841.0000.9991.0001.0000.1840.0620.0620.390
SPACE_NUMBER1.0001.0001.000NaN0.6001.0001.0000.5591.0000.6991.0001.0001.0001.0001.0001.0001.0000.6990.5591.000
VENDOR0.0860.0470.0001.0001.0000.9970.6750.0000.0000.0001.0000.9961.0000.1800.1841.0001.0000.0170.0120.402
X0.0240.0170.017-0.679-0.2160.0810.0170.2970.0171.0000.6990.017-0.8260.0520.0620.6990.0171.0000.311-0.826
Y0.0240.0240.012-0.395-0.1480.0810.0121.0000.0120.3130.5590.012-0.3770.0520.0620.5590.0120.3111.000-0.377
_id0.0000.1700.0810.822-0.1960.9680.385-0.3650.035-0.8271.0000.4021.0000.3330.3901.0000.402-0.826-0.3771.000

Missing values

2024-09-03T20:56:20.507196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-03T20:56:20.785065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-03T20:56:21.323333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_idXYOBJECTIDMETER_IDVENDORPAY_POLICYPRE_PAYPARK_NO_PAYGREEN_DOMETOW_AWAYSTREET_CLEANINGDIRBLK_NOSTREETLOCK_LOCK__LONGITUDELATITUDETRAVEL_DIRECTIONFROM_INTERSECTIONTO_INTERSECTIONSPACE_NUMBERNUMBEROFSPACESMETER_TYPEHAS_SENSORG_DISTRICTG_PASSPORT_ZONESG_PM_ZONEG_SUBZONEG_ZONEBASE_RATEPOLE_MOUNTYOKEHOUSING_TYPEHOUSING_MANUFACTURERSIDEWALKGECOIN_SLOTLEMETER_CONDITIONPERMIT_RATEINSTALLED_ONPURCHASED_DATEMETER_STATESPACE_STATE
01771671.61275432953700.433407721450001Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneNARLINEWBURY ST A-B1None-71.07268742.352311NoneNoneNone11MULTI-SPACE STALLNODISTRICT 0None8870MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
12771908.9892480522953745.355233892450002Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneSARLINEWBURY ST A-B1None-71.07180842.352431NoneNoneNone21MULTI-SPACE STALLNODISTRICT 0NoneNone0MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
23771592.2858132272953671.610630723450003Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneNARLINEWBURY ST A-B1None-71.07298142.352233NoneNoneNone31MULTI-SPACE STALLNODISTRICT 0NoneNone0MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
34771816.4438294772953711.363503984450004Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneSARLINEWBURY ST A-B1None-71.07215142.352339NoneNoneNone41MULTI-SPACE STALLNODISTRICT 0NoneNone0MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
45771502.9703750612953640.187465225450005Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneNARLINEWBURY ST A-B1None-71.07331242.352148NoneNoneNone51MULTI-SPACE STALLNODISTRICT 0NoneNone0MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
56771660.4972909692953653.002072146450006Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneSARLINEWBURY ST A-B1None-71.07272942.352181NoneNoneNone61MULTI-SPACE STALLNODISTRICT 0None8880MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
67771322.0944244712953593.729552897450007Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneNBERKNEWBURY ST B-C1None-71.07398242.352023NoneNoneNone71MULTI-SPACE STALLNODISTRICT 0None8890MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
78771350.8551937192953518.072223898450008Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneSBERKNEWBURY ST B-C1None-71.07387742.351815NoneNoneNone81MULTI-SPACE STALLNODISTRICT 0None8900MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
89771209.3200437282953550.52852389450009Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneNBERKNEWBURY ST B-C1None-71.074442.351906NoneNoneNone91MULTI-SPACE STALLNODISTRICT 0NoneNone0MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
910771229.9564853912953477.7458609810450010Parkeon08:00AM-08:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 08:00PM-24:00AM MON-SATNoneNoneNoneSBERKNEWBURY ST B-C1None-71.07432542.351706NoneNoneNone101MULTI-SPACE STALLNODISTRICT 0NoneNone0MSMS0.25NoneNoneNoneNoneNoneNoneNoneNone1/1/2017 12:00:00 AMNoneACTIVEACTIVE
_idXYOBJECTIDMETER_IDVENDORPAY_POLICYPRE_PAYPARK_NO_PAYGREEN_DOMETOW_AWAYSTREET_CLEANINGDIRBLK_NOSTREETLOCK_LOCK__LONGITUDELATITUDETRAVEL_DIRECTIONFROM_INTERSECTIONTO_INTERSECTIONSPACE_NUMBERNUMBEROFSPACESMETER_TYPEHAS_SENSORG_DISTRICTG_PASSPORT_ZONESG_PM_ZONEG_SUBZONEG_ZONEBASE_RATEPOLE_MOUNTYOKEHOUSING_TYPEHOUSING_MANUFACTURERSIDEWALKGECOIN_SLOTLEMETER_CONDITIONPERMIT_RATEINSTALLED_ONPURCHASED_DATEMETER_STATESPACE_STATE
69456946760928.8501191442953016.471192976946NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11244142.350575NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69466947760959.6869997232953011.873761226947NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11232742.350562NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69476948760990.5258488062953006.911828896948NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11221342.350548NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69486949761021.3627293852953002.314397146949NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11209942.350535NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69496950761052.1996099652952997.717293476950NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11198542.350522NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69506951761083.0368186382952993.119861726951NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11187142.350509NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69516952761113.8756677212952988.158257476952NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11175742.350495NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69526953761144.7125483012952983.561153816953NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11164342.350482NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69536954761175.5497569742952978.964050146954NoneIPS08:00AM-06:00PM MON-SAT $0.25 120None00:00AM-24:00AM SUN, 00:00AM-08:00AM MON-SAT, 06:00PM-24:00AM MON-SATNoneNoneNoneSAMORCOMMONWEALTH AV1None-71.11152942.350469NoneNoneNoneNone1SINGLE-SPACENODISTRICT 0NoneNone0KEKE0.25NoneNoneNoneNoneNoneNoneNoneNone4/1/2017 1:00:00 AMNoneACTIVEACTIVE
69546955NoneNone6955NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone